Geographical Analysis of media
5. Widgets who what when where
Objective
The aim of this section is to present the different widgets used for the exploration of hypercubes and developped during the ODYCCEUS project. We adapt a little the initial programs for the case of octocubes that are used in IMAGEUN but the principles remains the same. Each widget will export a dataframe and a plotly figure, making possible to store the results in javascript and/or to use the table for development with another software.
Preparation
Load multilevel octocubes and transform in hypercubes
We load the octocubes at different levels of time agregation and transform them in hypercubes by removing the dual dimensions of states and regions. But we keep the order dimenson in order to be able to select only titles without description.
base<-readRDS("hypercubes/hc_mycorpus_covid_states_regions_multilevel.RDS")
hc_day<-base$day[,.(tag=sum(tags),news=sum(news)),.(order,who,what,when,states=states1,regions=regions1)]
hc_week<-base$week[,.(tag=sum(tags),news=sum(news)),.(order,who,what,when,states=states1,regions=regions1)]
hc_month<-base$month[,.(tag=sum(tags),news=sum(news)),.(order,who,what,when,states=states1,regions=regions1)]
hc_quarter<-base$quarter[,.(tag=sum(tags),news=sum(news)),.(order,who,what,when,states=states1,regions=regions1)]
hc_year<-base$year[,.(tag=sum(tags),news=sum(news)),.(order,who,what,when,states=states1,regions=regions1)]Load statistical test function
#### ---------------- testchi2 ----------------
#' @title Compute the average salience of the topic and test significance of deviation
#' @name what
#' @description create a table and graphic of the topic
#' @param tabtest a table with variable trial, success and null.value
#' @param minsamp : Threshold of sample size requested for salience computation
#' @param mintest : Threshold of estimated value requested for chi-square test
testchi2<-function(tabtest=tabtest,
minsamp = 20,
mintest = 5)
{
tab<-tabtest
n<-dim(tab)[1]
# Compute salience if sample size sufficient (default : N>20)
tab$estimate <-NA
tab$salience <-NA
tab$chi2<-NA
tab$p.value<-NA
tab$estimate<-round(tab$success/tab$trial,5)
tab$salience<-tab$estimate/tab$null.value
# Chi-square test if estimated value sufficient (default : Nij* > 5)
for (i in 1:n) {
if(tab$trial[i]*tab$null.value[i]>=mintest) {
test<-prop.test(x=tab$success[i],n=tab$trial[i], p=tab$null.value[i],
alternative = "greater")
tab$chi2[i]<-round(test$statistic,2)
tab$p.value[i]<-round(test$p.value,5)
}
}
# }
return(tab)
}What
Function
### ---------------- what ----------------
#' @title Compute the average salience of the topic
#' @name what
#' @description create a table and graphic of the topic
#' @param hc an hypercube prepared as data.table
#' @param subtop a subtag of the main tag (default = NA)
#' @param title Title of the graphic
what <- function (hc = hypercube,
what = "what",
subtop = NA,
title = "What ?")
{
tab<-hc
tab$what<-tab[[what]]
if (is.na(subtop)){tab$what <-tab$what !="_no_"}else {tab$what <- tab$what == subtop}
tab<-tab[,list(news = sum(news)),by = what]
tab$pct<-100*tab$news/sum(tab$news)
p <- plot_ly(tab,
labels = ~what,
values = ~pct,
type = 'pie') %>%
layout(title = title,
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
output<-list("table" = tab, "plotly" =p)
return(output)
}Application 1 : covid topic
res<-what(hc_year)
res$tableFALSE what news pct
FALSE 1: FALSE 3110692 98.261071
FALSE 2: TRUE 55050 1.738929res$plotlyApplication n°2 : state subtopic
res <-hc_year %>% filter(states != "_no_") %>%
what(what = "states",
subtop ="UKR",
title = "Share of Ukraine in international news")
res$tableFALSE what news pct
FALSE 1: FALSE 599035.74 97.568862
FALSE 2: TRUE 14926.26 2.431138res$plotlyApplication n°3 : macroregion subtopic
res <-hc_year %>% filter(regions != "_no_") %>%
what(what = "regions",
subtop ="OR_EU",
title = "Share of EU in macroregional news")
res$tableFALSE what news pct
FALSE 1: FALSE 56153.67 69.34606
FALSE 2: TRUE 24822.33 30.65394res$plotlyWho.What
function
#### ---------------- who.what ----------------
#' @title visualize variation of the topic between media
#' @name who.what
#' @description create a table of variation of the topic by media
#' @param hc an hypercube prepared as data.table
#' @param test : visualize test (TRUE) or salience (FALSE)
#' @param minsamp : Threshold of sample size requested for salience computation
#' @param mintest sample size of estimate for chi-square test (default = 5)
#' @param title Title of the graphic
who.what <- function (hc = hypercube,
what = "what",
subtop = NA,
test = FALSE,
minsamp = 20,
mintest = 5,
title = "Who says What ?")
{
tab<-hc
tab$what<-tab[[what]]
if (is.na(subtop)){tab$what <-tab$what !="_no_"}else {tab$what <- tab$what == subtop}
# {tab$what <-tab$what !="_no_"}
tab<-tab[,list(trial = sum(news),success=round(sum(news*what),0)),by = list(who)]
ref <-round(sum(tab$success)/sum(tab$trial),4)
tab$null.value<-ref
tab<-testchi2(tabtest=tab,
minsamp = minsamp,
mintest = mintest)
if (test==FALSE) {tab$index =tab$salience
tab$index[tab$index>4]<-4
tab<-tab[tab$trial > minsamp,]
mycol<-brewer.pal(7,"YlOrRd")
}
else {tab$index=1-tab$p.value
tab<-tab[tab$trial*tab$null.value>mintest,]
mycol<-rev(brewer.pal(7,"RdYlBu"))
mycol[4]<-"lightyellow"
}
p <- plot_ly(tab,
x = ~who,
y = ~estimate*100,
color= ~index,
colors= mycol,
hovertemplate = ~paste('Source: ',who,
'<br /> Total news : ', round(trial,0),
'<br /> Topic news : ', round(success,0),
'<br /> % observed : ', round(estimate*100,2),'%',
'<br /> % estimated : ', round(null.value*100,2),'%',
'<br /> Salience : ', round(salience,2),
'<br /> p.value : ', round(p.value,4)),
type = "bar") %>%
layout(title = title,
yaxis = list(title = "% news"),
barmode = 'stack')
output<-list("table" = tab, "plotly" =p)
return(output)
}Applicaton n°1 : Covid Topic
An example of computation of the share of a non spatial topic (Covid) in the full sample of news during the years 2020 and 2021
res <- hc_year %>%
filter(when >= as.Date("2020-01-01"), when <=as.Date("2021-12-31")) %>%
who.what( what = "what",
title = "Share of Covid in total news (2020-2021)",
test=TRUE)
res$tableFALSE who trial success null.value estimate salience chi2 p.value
FALSE 1: DEU_suddeu 116917 9997 0.0931 0.08551 0.9184748 79.79 1
FALSE 2: FRA_figaro 161430 14457 0.0931 0.08956 0.9619764 23.97 1
FALSE 3: TUN_afrman 51195 8387 0.0931 0.16382 1.7596133 3032.07 0
FALSE 4: TUR_dunya 187927 15350 0.0931 0.08168 0.8773362 290.11 1
FALSE index
FALSE 1: 0
FALSE 2: 0
FALSE 3: 1
FALSE 4: 0res$plotly- Comment : The share of news where the covid topic is present is significantly higher in the African Manager (16.4%) as compared to the three other newspapers 8 to 9%) .
Application n°2 : State subtopic
An example of computation of the share of a national subtopic (Ukraine) in the sample of news where the topic is present (news with at least one state mentionned) since January 2021
res <-hc_year %>% filter(states != "_no_") %>%
filter(when >= as.Date("2022-01-01")) %>%
who.what(what = "states",
subtop ="UKR",
title = "Share of Ukraine in international news 2022-2023",
test=TRUE)
res$tableFALSE who trial success null.value estimate salience chi2 p.value index
FALSE 1: DEU_suddeu 10562 2763 0.1112 0.26160 2.3525180 2415.73 0 1
FALSE 2: FRA_figaro 17540 2206 0.1112 0.12577 1.1310252 37.52 0 1
FALSE 3: TUN_afrman 11778 754 0.1112 0.06402 0.5757194 264.81 1 0
FALSE 4: TUR_dunya 25508 1549 0.1112 0.06073 0.5461331 657.00 1 0res$plotly- Comment : The share of international news where the Ukraine topic is present is significantly higher in the Süddeutche Zeitung (26.2%) and to a lesser extent in Le Figaro (12.6%) as compared to the level observed in the two other newspapers 6 to 7%) .
Application n°3 : Macroregion subtopic
Same example applied to macroregion : what is the share of the subtopic European Union in the subsample news where at least one macroregion is mentionned for the whole period 2013-2023
res <-hc_year %>% filter(regions != "_no_") %>%
who.what(what = "regions",
subtop ="OR_EU",
title = "Share of EU in macroregional news (2013-2023)",
test=TRUE)
res$tableFALSE who trial success null.value estimate salience chi2 p.value index
FALSE 1: DEU_suddeu 18810 8939 0.3065 0.47523 1.5505057 2518.48 0 1
FALSE 2: FRA_figaro 29793 7975 0.3065 0.26768 0.8733442 211.04 1 0
FALSE 3: TUN_afrman 7160 1630 0.3065 0.22765 0.7427406 209.04 1 0
FALSE 4: TUR_dunya 25213 6278 0.3065 0.24900 0.8123980 391.93 1 0res$plotly- Comment : The share of macroregional news where European Union is mentionned is significantly higher in Süddeutsche Zeitung (47.5%) as compared to the three other newspapers (22 to 27%)
When.What
function
#### ---------------- when.what ----------------
#' @title visualize variation of the topic through time
#' @name when.what
#' @description create a table of variation of the topic by media
#' @param test : visualize test (TRUE) or salience (FALSE)
#' @param minsamp : Threshold of sample size requested for salience computation
#' @param mintest sample size of estimate for chi-square test (default = 5)
#' @param title Title of the graphic
when.what <- function (hc = hypercube,
what = "what",
subtop = NA,
test = FALSE,
minsamp = 20,
mintest = 5,
title = "When is said What ?")
{
tab<-hc
tab$what<-tab[[what]]
if (is.na(subtop)){tab$what <-tab$what !="_no_"}else {tab$what <- tab$what == subtop}
# {tab$what <-tab$what !="_no_"}
tab<-tab[,list(trial = sum(news),success=round(sum(news*what),0)),by = list(when)]
ref <-round(sum(tab$success)/sum(tab$trial),4)
tab$null.value<-ref
tab<-testchi2(tabtest=tab,
minsamp = minsamp,
mintest = mintest)
tab<-tab[order(when),]
if (test==FALSE) {tab$index =tab$salience
tab<-tab[tab$trial > minsamp,]
mycol<-brewer.pal(7,"YlOrRd")
}
else {tab$index=tab$p.value
tab<-tab[tab$trial*tab$null.value>mintest,]
mycol<-brewer.pal(7,"RdYlBu")
mycol[4]<-"lightyellow"
}
p <- plot_ly(tab,
x = ~as.character(when),
y = ~estimate*100,
color= ~index,
colors= mycol,
# hoverinfo = "text",
hovertemplate = ~paste('Time: ',when,
'<br /> Total news : ', round(trial,0),
'<br /> Topic news : ', round(success,0),
'<br /> % observed : ', round(estimate*100,2),'%',
'<br /> % estimated : ', round(null.value*100,2),'%',
'<br /> Salience : ', round(salience,2),
'<br /> p.value : ', round(p.value,4)),
type = "bar") %>%
layout(title = title,
yaxis = list(title = "% news"),
barmode = 'stack')
output<-list("table" = tab, "plotly" =p)
return(output)
}Applicaton n°1 : Covid Topic
An example of computation of the share of a non spatial topic (Covid) in the full sample of news by week for one media in 2020-2021
res <- hc_week %>% filter(who=="DEU_suddeu") %>%
filter(when >= as.Date("2020-01-01"), when <=as.Date("2021-12-31")) %>%
when.what(what = "what",
title = "Share of Covid-19 topic in news published by Süddeutsche Zeitung (2020-21)",
test=TRUE)
head(res$table)FALSE when trial success null.value estimate salience chi2 p.value index
FALSE 1: 2020-01-06 903 3 0.0858 0.00332 0.03869464 77.26 1.0000 1.0000
FALSE 2: 2020-01-13 924 2 0.0858 0.00216 0.02517483 81.34 1.0000 1.0000
FALSE 3: 2020-01-20 1027 33 0.0858 0.03213 0.37447552 37.03 1.0000 1.0000
FALSE 4: 2020-01-27 973 53 0.0858 0.05447 0.63484848 11.78 0.9997 0.9997
FALSE 5: 2020-02-03 906 38 0.0858 0.04194 0.48881119 21.66 1.0000 1.0000
FALSE 6: 2020-02-10 939 32 0.0858 0.03408 0.39720280 31.37 1.0000 1.0000res$plotlyApplication n°2 : State subtopic
Example of analysis of the share of news about Ukraine among news mentionning one country, by week, for Le Figaro in 2022
res <-hc_week %>% filter(states != "_no_") %>%
filter(who=="FRA_figaro") %>%
# filter(when >= as.Date("2022-01-01")) %>%
when.what(what = "states",
subtop ="UKR",
title = "Share of Ukraine in international news published by Le Figaro ",
test=TRUE)
head(res$table)FALSE when trial success null.value estimate salience chi2 p.value
FALSE 1: 2013-04-08 532 0 0.024 0.00000 0.00000000 12.08 0.99974
FALSE 2: 2013-04-15 705 1 0.024 0.00142 0.05916667 14.40 0.99993
FALSE 3: 2013-04-22 854 0 0.024 0.00000 0.00000000 19.99 1.00000
FALSE 4: 2013-04-29 678 2 0.024 0.00295 0.12291667 11.94 0.99973
FALSE 5: 2013-05-06 736 0 0.024 0.00000 0.00000000 17.09 0.99998
FALSE 6: 2013-05-13 745 0 0.024 0.00000 0.00000000 17.31 0.99998
FALSE index
FALSE 1: 0.99974
FALSE 2: 0.99993
FALSE 3: 1.00000
FALSE 4: 0.99973
FALSE 5: 0.99998
FALSE 6: 0.99998res$plotlyApplication n°3 : Macroregion subtopic
Example of analysis of the share of news about European news among news mentioning one macroregion, by quarter, for Dunya during the whole period
res <-hc_month %>% filter(regions != "_no_") %>% filter(who=="TUR_dunya") %>%
when.what(what = "regions",
subtop ="OR_EU",
title = "Share of EU in macroregional news published by Dunya (2013-2023)",
test=TRUE)
head(res$table)FALSE when trial success null.value estimate salience chi2 p.value index
FALSE 1: 2013-06-01 76 23 0.249 0.30263 1.2153815 0.90 0.17142 0.17142
FALSE 2: 2013-07-01 44 11 0.249 0.25000 1.0040161 0.00 0.50000 0.50000
FALSE 3: 2013-08-01 40 6 0.249 0.15000 0.6024096 1.60 0.89708 0.89708
FALSE 4: 2013-09-01 37 7 0.249 0.18919 0.7597992 0.42 0.74255 0.74255
FALSE 5: 2013-10-01 83 33 0.249 0.39759 1.5967470 9.02 0.00133 0.00133
FALSE 6: 2013-11-01 132 50 0.249 0.37879 1.5212450 11.21 0.00041 0.00041res$plotlyWhere.What
function
#### ---------------- where.what ----------------
#' @title visualize spatialization of the topic
#' @name where.what
#' @description create a table of variation of the topic by media
#' @param hc an hypercube prepared as data.table
#' @param test : visualize test (TRUE) or salience (FALSE)
#' @param minsamp : Threshold of sample size requested for salience computation
#' @param mintest sample size of estimate for chi-square test (default = 5)
#' @param map a map with coordinates in lat-long
#' @param proj a projection accepted by plotly
#' @param title Title of the graphic
where.what <- function (hc = hypercube,
what = "what",
where = "where",
subtop = NA,
test = FALSE,
minsamp = 20,
mintest = 5,
map = world_ctr,
proj = 'azimuthal equal area',
title = "Where said What ?")
{
tab<-hc
tab$what<-tab[[what]]
tab$where<-tab[[where]]
if (is.na(subtop)){tab$what <-tab$what !="_no_"}else {tab$what <- tab$what == subtop}
# {tab$what <-tab$what !="_no_"}
tab<-tab[,list(trial = round(sum(news),0),success=round(sum(news*what),0)),by = list(where)]
ref <-round(sum(tab$success)/sum(tab$trial),4)
tab$null.value<-ref
tab<-testchi2(tabtest=tab,
minsamp = minsamp,
mintest = mintest)
tab<-tab[order(-chi2),]
if (test==FALSE) {tab$index =tab$salience
tab<-tab[tab$trial > minsamp,]
mycol<-brewer.pal(7,"YlOrRd")
} else {tab$index=tab$p.value
tab<-tab[tab$trial*tab$null.value>mintest,]
mycol<-brewer.pal(7,"RdYlBu")
mycol[4]<-"lightyellow"
}
map<-merge(map,tab,all.x=T,all.y=F,by.x="ISO3",by.y="where")
#map2<-map[is.na(map$pct)==F,]
#map2<-st_centroid(map2)
#map2<-st_drop_geometry(map2)
g <- list(showframe = TRUE,
framecolor= toRGB("gray20"),
coastlinecolor = toRGB("gray20"),
showland = TRUE,
landcolor = toRGB("gray50"),
showcountries = TRUE,
countrycolor = toRGB("white"),
countrywidth = 0.2,
projection = list(type = proj))
p<- plot_geo(map)%>%
add_markers(x = ~lon,
y = ~lat,
sizes = c(0, 250),
size = ~success,
# color= ~signif,
color = ~index,
colors= mycol,
# hoverinfo = "text",
hovertemplate = ~paste('Location: ',NAME,
'<br /> Total news : ', round(trial,0),
'<br /> Topic news : ', round(success,0),
'<br /> % observed : ', round(estimate*100,2),'%',
'<br /> % estimated : ', round(null.value*100,2),'%',
'<br /> Salience : ', round(salience,2),
'<br /> p.value : ', round(p.value,4))) %>%
layout(geo = g,
title = title)
output<-list("table" = tab, "plotly" =p)
return(output)
}Applicaton n°1 : Covid Topic
An example of computation of the share of a non spatial topic (Covid) in the full sample of news by week for one media.
world_ctr<-readRDS("map/world_ctr_4326.Rdata")
res <- hc_week %>% filter(who=="DEU_suddeu") %>%
filter(states != "DEU") %>%
filter(states != "_no_") %>%
filter(when >= as.Date("2020-01-01"), when <=as.Date("2021-12-31")) %>%
where.what(what = "what",
where ="states",
title = "Share of Covid-19 topic in foreign news published by Süddeutsche Zeitung in 2020-2021",
minsamp = 5,
mintest = 2,
test=TRUE)
#res$table
res$plotlyApplication n°2 : National subtopic
Impossible to use state as subtopic in this case
Application n°3 : Regional subtopic
Example of analysis of the share of news about Africa in news associated to one country
res <-hc_quarter %>% filter(regions != "_no_") %>%
filter(who=="TUR_dunya") %>%
filter(states!="TUR") %>%
where.what(what = "regions",
subtop = "OR_EU",
where = "states",
title = "Share of EU in macroregional news published by Dunya (2013-2023)",
test=TRUE,
mintest=1,
minsamp=5)
head(res$table)FALSE where trial success null.value estimate salience chi2 p.value index
FALSE 1: GBR 191 120 0.2465 0.62827 2.5487627 147.83 0 0
FALSE 2: BEL 166 93 0.2465 0.56024 2.2727789 86.29 0 0
FALSE 3: RUS 660 246 0.2465 0.37273 1.5120892 55.94 0 0
FALSE 4: _no_ 16377 3657 0.2465 0.22330 0.9058824 47.33 1 1
FALSE 5: IRL 36 26 0.2465 0.72222 2.9298986 41.34 0 0
FALSE 6: IRN 123 56 0.2465 0.45528 1.8469777 27.75 0 0res$plotlywhen.who.what
function
#### ---------------- when.who.what ----------------
#' @title visualize variation of the topic by media through time
#' @name when.who.what
#' @description create a table of variation of the topic by media through time
#' @param hc an hypercube prepared as data.table
#' @param test : visualize test (TRUE) or salience (FALSE)
#' @param minsamp : Threshold of sample size requested for salience computation
#' @param mintest sample size of estimate for chi-square test (default = 5)
#' @param title Title of the graphic
when.who.what <- function (hc = hypercube,
what = "what",
subtop = NA,
test = FALSE,
minsamp = 20,
mintest = 5,
title = "What by Whom and When ?")
{
tab<-hc
tab$what<-tab[[what]]
if (is.na(subtop)){tab$what <-tab$what !="_no_"}else {tab$what <- tab$what == subtop}
# {tab$what <-tab$what !="_no_"}
tab<-tab[is.na(when)==F,]
tab<-tab[,list(trial = sum(news),success=round(sum(news*what),0)),by = list(when,who)]
ref<-tab[,list(null.value = round(sum(success)/sum(trial),4)), by = list(who)]
tab<-merge(tab,ref,by="who")
tab<-testchi2(tabtest=tab,
minsamp = minsamp,
mintest = mintest)
if (test==FALSE) {tab$index =tab$salience
tab<-tab[tab$trial > minsamp,]
mycol<-brewer.pal(7,"YlOrRd")
}
else {tab$index=tab$p.value
tab<-tab[tab$trial*tab$null.value>mintest,]
mycol<-brewer.pal(7,"RdYlBu")
mycol[4]<-"lightyellow"
}
p <- plot_ly(tab,
x = ~when,
y = ~who,
z= ~index,
sizes = c(0, 250),
size = ~success,
colors= mycol,
hovertext = ~paste( 'Total news : ', round(trial,0),
'<br /> Topic news : ', round(success,0),
'<br /> % observed : ', round(estimate*100,2),'%',
'<br /> % estimated : ', round(null.value*100,2),'%',
'<br /> Salience : ', round(salience,2),
'<br /> p.value : ', round(p.value,4)),
# name = ~tags,
type = "heatmap") %>%
layout(title = title,
yaxis = list(title = "media"),
xaxis = list(title = "time"))
p
output<-list("table" = tab, "plotly" =p)
return(output)
}Applicaton n°1 : Covid Topic
An example of computation of the share of a non spatial topic (Covid) in the full sample of news by week for one media.
res <- hc_week %>% filter(when >= as.Date("2019-01-01"), when <=as.Date("2022-12-31")) %>%
when.who.what(what = "what",
title = "Share of Covid-19 topic in news published by newspapers (2019-2022)",
test=TRUE)
head(res$table)FALSE who when trial success null.value estimate salience chi2
FALSE 1: DEU_suddeu 2021-09-27 1905 55 0.0494 0.02887 0.5844130 16.66
FALSE 2: DEU_suddeu 2021-10-04 3764 159 0.0494 0.04224 0.8550607 3.96
FALSE 3: DEU_suddeu 2021-10-11 3558 129 0.0494 0.03626 0.7340081 12.81
FALSE 4: DEU_suddeu 2021-10-18 3552 135 0.0494 0.03801 0.7694332 9.58
FALSE 5: DEU_suddeu 2021-10-25 3130 121 0.0494 0.03866 0.7825911 7.46
FALSE 6: DEU_suddeu 2021-11-01 2711 150 0.0494 0.05533 1.1200405 1.91
FALSE p.value index
FALSE 1: 0.99998 0.99998
FALSE 2: 0.97664 0.97664
FALSE 3: 0.99983 0.99983
FALSE 4: 0.99902 0.99902
FALSE 5: 0.99685 0.99685
FALSE 6: 0.08371 0.08371res$plotlyApplicaton n°2 : Ukraine in international news (by week)
How many news about Ukraine in international news (i.e. news mentioning at least one country) ?
res <-hc_month %>% filter(states != "_no_") %>%
filter(states != substr(who,1,3)) %>%
when.who.what(what = "states",
subtop="UKR",
title = "Share of Ukraine in international news (2013-2023)",
minsamp = 5,
mintest = 1,
test=TRUE)
head(res$table)FALSE who when trial success null.value estimate salience
FALSE 1: DEU_suddeu 2021-10-01 753.8333 4 0.0558 0.00531 0.09516129
FALSE 2: DEU_suddeu 2021-11-01 615.2167 10 0.0558 0.01625 0.29121864
FALSE 3: DEU_suddeu 2021-12-01 634.1389 30 0.0558 0.04731 0.84784946
FALSE 4: DEU_suddeu 2022-01-01 682.5833 79 0.0558 0.11574 2.07419355
FALSE 5: DEU_suddeu 2022-02-01 915.1833 233 0.0558 0.25459 4.56254480
FALSE 6: DEU_suddeu 2022-03-01 1668.8167 916 0.0558 0.54889 9.83673835
FALSE chi2 p.value index
FALSE 1: 35.53 1.00000 1.00000
FALSE 2: 17.52 0.99999 0.99999
FALSE 3: 0.71 0.80098 0.80098
FALSE 4: 45.41 0.00000 0.00000
FALSE 5: 682.69 0.00000 0.00000
FALSE 6: 7691.98 0.00000 0.00000res$plotlyApplicaton n°3 : EU in macroregional news (by quarter)
How many news about EU in macroregional news (i.e. news mentioning at least one macroregion) ?
res <- hc_month %>% filter(regions !="_no_") %>%
when.who.what(what = "regions", subtop="OR_EU",
title = "Share of EU in macroregional news",
minsamp = 5,
mintest = 2,
test=TRUE)
head(res)FALSE $table
FALSE who when trial success null.value estimate salience chi2
FALSE 1: DEU_suddeu 2021-10-01 195 102 0.4749 0.52308 1.1014529 1.63
FALSE 2: DEU_suddeu 2021-11-01 193 100 0.4749 0.51813 1.0910297 1.28
FALSE 3: DEU_suddeu 2021-12-01 158 71 0.4749 0.44937 0.9462413 0.32
FALSE 4: DEU_suddeu 2022-01-01 177 82 0.4749 0.46328 0.9755317 0.05
FALSE 5: DEU_suddeu 2022-02-01 217 89 0.4749 0.41014 0.8636344 3.39
FALSE ---
FALSE 467: TUR_dunya 2022-11-01 197 47 0.2490 0.23858 0.9581526 0.07
FALSE 468: TUR_dunya 2022-12-01 507 176 0.2490 0.34714 1.3941365 25.59
FALSE 469: TUR_dunya 2023-01-01 484 75 0.2490 0.15496 0.6223293 22.39
FALSE 470: TUR_dunya 2023-02-01 216 54 0.2490 0.25000 1.0040161 0.00
FALSE 471: TUR_dunya 2023-03-01 249 46 0.2490 0.18474 0.7419277 5.16
FALSE p.value index
FALSE 1: 0.10107 0.10107
FALSE 2: 0.12909 0.12909
FALSE 3: 0.71330 0.71330
FALSE 4: 0.59266 0.59266
FALSE 5: 0.96729 0.96729
FALSE ---
FALSE 467: 0.60097 0.60097
FALSE 468: 0.00000 0.00000
FALSE 469: 1.00000 1.00000
FALSE 470: 0.50000 0.50000
FALSE 471: 0.98845 0.98845
FALSE
FALSE $plotlyres$plotlyApplicaton n°3bis : NATO in macroregional news (by year)
How many news about EU in macroregional news (i.e. news mentioning at least one macroregion) ?
res <- hc_year %>% filter(regions !="_no_") %>%
when.who.what(what = "regions", subtop="OR_NATO",
title = "Share of NATO in macroregional news",
minsamp = 5,
mintest = 2,
test=TRUE)
head(res)FALSE $table
FALSE who when trial success null.value estimate salience chi2
FALSE 1: DEU_suddeu 2021-01-01 1294 69 0.0490 0.05332 1.0881633 0.43
FALSE 2: DEU_suddeu 2022-01-01 1456 180 0.0490 0.12363 2.5230612 172.41
FALSE 3: DEU_suddeu 2020-01-01 1117 31 0.0490 0.02775 0.5663265 10.37
FALSE 4: DEU_suddeu 2013-01-01 1345 32 0.0490 0.02379 0.4855102 17.80
FALSE 5: DEU_suddeu 2014-01-01 1775 98 0.0490 0.05521 1.1267347 1.34
FALSE 6: DEU_suddeu 2015-01-01 3048 133 0.0490 0.04364 0.8906122 1.77
FALSE 7: DEU_suddeu 2016-01-01 2361 93 0.0490 0.03939 0.8038776 4.48
FALSE 8: DEU_suddeu 2017-01-01 2176 106 0.0490 0.04871 0.9940816 0.00
FALSE 9: DEU_suddeu 2018-01-01 2547 106 0.0490 0.04162 0.8493878 2.82
FALSE 10: DEU_suddeu 2019-01-01 1690 73 0.0490 0.04320 0.8816327 1.10
FALSE 11: FRA_figaro 2021-01-01 1695 40 0.0452 0.02360 0.5221239 17.83
FALSE 12: FRA_figaro 2022-01-01 1939 226 0.0452 0.11655 2.5785398 227.11
FALSE 13: FRA_figaro 2020-01-01 2271 56 0.0452 0.02466 0.5455752 21.73
FALSE 14: FRA_figaro 2013-01-01 2442 63 0.0452 0.02580 0.5707965 20.85
FALSE 15: FRA_figaro 2014-01-01 3733 283 0.0452 0.07581 1.6772124 80.34
FALSE 16: FRA_figaro 2015-01-01 4215 155 0.0452 0.03677 0.8134956 6.74
FALSE 17: FRA_figaro 2016-01-01 3938 116 0.0452 0.02946 0.6517699 22.25
FALSE 18: FRA_figaro 2017-01-01 3675 184 0.0452 0.05007 1.1077434 1.91
FALSE 19: FRA_figaro 2018-01-01 2902 98 0.0452 0.03377 0.7471239 8.52
FALSE 20: FRA_figaro 2019-01-01 2684 96 0.0452 0.03577 0.7913717 5.32
FALSE 21: FRA_figaro 2023-01-01 299 31 0.0452 0.10368 2.2938053 22.36
FALSE 22: TUN_afrman 2013-01-01 226 0 0.0209 0.00000 0.0000000 3.86
FALSE 23: TUN_afrman 2014-01-01 454 4 0.0209 0.00881 0.4215311 2.68
FALSE 24: TUN_afrman 2015-01-01 529 27 0.0209 0.05104 2.4421053 22.03
FALSE 25: TUN_afrman 2016-01-01 908 16 0.0209 0.01762 0.8430622 0.33
FALSE 26: TUN_afrman 2017-01-01 718 10 0.0209 0.01393 0.6665072 1.38
FALSE 27: TUN_afrman 2018-01-01 645 14 0.0209 0.02171 1.0387560 0.00
FALSE 28: TUN_afrman 2019-01-01 530 2 0.0209 0.00377 0.1803828 6.78
FALSE 29: TUN_afrman 2020-01-01 561 4 0.0209 0.00713 0.3411483 4.55
FALSE 30: TUN_afrman 2021-01-01 852 10 0.0209 0.01174 0.5617225 3.06
FALSE 31: TUN_afrman 2022-01-01 1292 37 0.0209 0.02864 1.3703349 3.41
FALSE 32: TUN_afrman 2023-01-01 445 26 0.0209 0.05843 2.7956938 28.82
FALSE 33: TUR_dunya 2019-01-01 3428 141 0.0406 0.04113 1.0130542 0.01
FALSE 34: TUR_dunya 2020-01-01 5006 57 0.0406 0.01139 0.2805419 108.93
FALSE 35: TUR_dunya 2021-01-01 4868 112 0.0406 0.02301 0.5667488 38.23
FALSE 36: TUR_dunya 2022-01-01 4453 262 0.0406 0.05884 1.4492611 37.55
FALSE 37: TUR_dunya 2013-01-01 547 21 0.0406 0.03839 0.9455665 0.02
FALSE 38: TUR_dunya 2014-01-01 1188 80 0.0406 0.06734 1.6586207 21.13
FALSE 39: TUR_dunya 2015-01-01 1046 50 0.0406 0.04780 1.1773399 1.21
FALSE 40: TUR_dunya 2016-01-01 1197 58 0.0406 0.04845 1.1933498 1.70
FALSE 41: TUR_dunya 2017-01-01 1495 94 0.0406 0.06288 1.5487685 18.48
FALSE 42: TUR_dunya 2018-01-01 1036 81 0.0406 0.07819 1.9258621 36.61
FALSE 43: TUR_dunya 2023-01-01 949 68 0.0406 0.07165 1.7647783 22.71
FALSE who when trial success null.value estimate salience chi2
FALSE p.value index
FALSE 1: 0.25591 0.25591
FALSE 2: 0.00000 0.00000
FALSE 3: 0.99936 0.99936
FALSE 4: 0.99999 0.99999
FALSE 5: 0.12358 0.12358
FALSE 6: 0.90826 0.90826
FALSE 7: 0.98280 0.98280
FALSE 8: 0.50491 0.50491
FALSE 9: 0.95353 0.95353
FALSE 10: 0.85293 0.85293
FALSE 11: 0.99999 0.99999
FALSE 12: 0.00000 0.00000
FALSE 13: 1.00000 1.00000
FALSE 14: 1.00000 1.00000
FALSE 15: 0.00000 0.00000
FALSE 16: 0.99529 0.99529
FALSE 17: 1.00000 1.00000
FALSE 18: 0.08366 0.08366
FALSE 19: 0.99825 0.99825
FALSE 20: 0.98944 0.98944
FALSE 21: 0.00000 0.00000
FALSE 22: 0.97523 0.97523
FALSE 23: 0.94915 0.94915
FALSE 24: 0.00000 0.00000
FALSE 25: 0.71725 0.71725
FALSE 26: 0.88012 0.88012
FALSE 27: 0.49786 0.49786
FALSE 28: 0.99540 0.99540
FALSE 29: 0.98351 0.98351
FALSE 30: 0.95993 0.95993
FALSE 31: 0.03237 0.03237
FALSE 32: 0.00000 0.00000
FALSE 33: 0.45442 0.45442
FALSE 34: 1.00000 1.00000
FALSE 35: 1.00000 1.00000
FALSE 36: 0.00000 0.00000
FALSE 37: 0.56097 0.56097
FALSE 38: 0.00000 0.00000
FALSE 39: 0.13529 0.13529
FALSE 40: 0.09617 0.09617
FALSE 41: 0.00001 0.00001
FALSE 42: 0.00000 0.00000
FALSE 43: 0.00000 0.00000
FALSE p.value index
FALSE
FALSE $plotlyres$plotly